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Abstract

Federated learning (FL) enables collaborative model training without sharing raw patient data, but standard approaches such as FedAvg treat each client as a black box and provide no mechanism for isolating an adversarial contributor, auditing per-client influence, or honouring a departed participants right to be forgotten. We present FED-FBD ( Federated Functional Block Diversification), a modular federated architecture that decomposes a ResNet backbone into six functional blocks (the stem, four residual groups, and the classification head) and maintains a warehouse of N colour variants, each assembled from independently tracked and contributorstamped blocks. FED-FBD provides three capabilities absent in FedAvg: (i) architecturally guaranteed block-level isolation , so that an adversarial or mislabelled client cannot contaminate the clean colours; (ii) privacy-by-design , where membership inference advantage is already indistinguishable from chance before any privacy mechanism is applied; and (iii) surgical machine unlearning of a departed participants contribution at sub-second cost and without retraining. Experiments on six MedMNIST-2D datasets, PathMNIST at 224 __ 224, and CIFAR-10 show that FED-FBD trades a modest 0 . 3%3 . 1% IID accuracy gap on the adequately sized datasets for these guarantees, remains within 0 . 8%4 . 0% of FedAvg at Dirichlet __ =1 . 0 on three of four datasets, and confines all six adversarial attacks we study to the poisoned clients own blocks with at most __ 0 . 01 AUC drift on the clean colours.